SchNetPack 2.0: A neural network toolbox for atomistic machine learning

Learning local equivariant representations for large-scale atomistic dynamics

(2023) Albert Musaelian et al.   Nature Communications

Accurate global machine learning force fields for molecules with hundreds of atoms

(2023) Stefan Chmiela et al.   Science Advances

Inverse design of 3d molecular structures with conditional generative neural networks

(2022) Niklas W. A. Gebauer et al.   Nature Communications

Roadmap on Machine Learning in Electronic Structure

(2022) Heather Kulik et al.   Electronic Structure

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

(2022) Simon Batzner et al.   Nature Communications

Kohn-Sham Equations as Regularizer: Building Prior Knowledge into Machine-Learned Physics

(2021) Li Li et al.   PHYSICAL REVIEW LETTERS

Machine Learning Force Fields

(2021) Oliver T. Unke et al.   CHEMICAL REVIEWS

Four Generations of High-Dimensional Neural Network Potentials

(2021) Jörg Behler   CHEMICAL REVIEWS

TorchMD: A Deep Learning Framework for Molecular Simulations

(2021) Stefan Doerr et al.   Journal of Chemical Theory and Computation

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

(2021) John A. Keith et al.   CHEMICAL REVIEWS

LAMMPS - A flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

(2021) Aidan P. Thompson et al.   COMPUTER PHYSICS COMMUNICATIONS

Perspective on integrating machine learning into computational chemistry and materials science

(2021) Julia Westermayr et al.   JOURNAL OF CHEMICAL PHYSICS

3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds

(2021) Rajendra P. Joshi et al.   JOURNAL OF PHYSICAL CHEMISTRY B

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

(2021) Oliver T. Unke et al.   Nature Communications

Machine Learning for Molecular Simulation

(2020) Frank Noé et al.   Annual Review of Physical Chemistry

Quantum Chemistry in the Age of Machine Learning

(2020) Pavlo O. Dral   Journal of Physical Chemistry Letters

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

(2020) Max Veit et al.   JOURNAL OF CHEMICAL PHYSICS

Exploring chemical compound space with quantum-based machine learning

(2020) O. Anatole von Lilienfeld et al.   Nature Reviews Chemistry

Quantum machine learning using atom-in-molecule-based fragments selected on the fly

(2020) Bing Huang et al.   Nature Chemistry

Deep-neural-network solution of the electronic Schrödinger equation

(2020) Jan Hermann et al.   Nature Chemistry

Autonomous robotic nanofabrication with reinforcement learning

(2020) Philipp Leinen et al.   Science Advances

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

(2020) Denghui Lu et al.   COMPUTER PHYSICS COMMUNICATIONS

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

(2019) Kunal Ghosh et al.   Advanced Science

Unsupervised machine learning in atomistic simulations, between predictions and understanding

(2019) Michele Ceriotti   JOURNAL OF CHEMICAL PHYSICS

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

(2019) Oliver T. Unke et al.   Journal of Chemical Theory and Computation

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

(2019) K. T. Schütt et al.   Nature Communications

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber et al.   JOURNAL OF CHEMICAL PHYSICS

VAMPnets for deep learning of molecular kinetics

(2018) Andreas Mardt et al.   Nature Communications

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

i-PI 2.0: A universal force engine for advanced molecular simulations

(2018) Venkat Kapil et al.   COMPUTER PHYSICS COMMUNICATIONS

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

(2018) K. T. Schütt et al.   Journal of Chemical Theory and Computation

Machine Learning a General-Purpose Interatomic Potential for Silicon

(2018) Albert P. Bartók et al.   Physical Review X

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

The atomic simulation environment—a Python library for working with atoms

(2017) Ask Hjorth Larsen et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Accelerated path integral methods for atomistic simulations at ultra-low temperatures

(2016) Felix Uhl et al.   JOURNAL OF CHEMICAL PHYSICS

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

How to remove the spurious resonances from ring polymer molecular dynamics

(2014) Mariana Rossi et al.   JOURNAL OF CHEMICAL PHYSICS

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Computing vibrational spectra from ab initio molecular dynamics

(2013) Martin Thomas et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

The ORCA program system

(2011) Frank Neese   Wiley Interdisciplinary Reviews-Computational Molecular Science

Efficient stochastic thermostatting of path integral molecular dynamics

(2010) Michele Ceriotti et al.   JOURNAL OF CHEMICAL PHYSICS

Colored-Noise Thermostats à la Carte

(2010) Michele Ceriotti et al.   Journal of Chemical Theory and Computation

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS